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res_unet.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jun 24 11:25:07 2020
@author: kdutta01
"""
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, MaxPooling2D, concatenate, Input, Dropout, Add
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler
from tensorflow.keras import backend as K
from tensorflow.keras.metrics import Precision, Recall, AUC, Accuracy
K.set_image_data_format('channels_last')
smooth = 1
def dice_coef(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_loss(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (1 -(2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth))
pr_metric = AUC(curve='PR', num_thresholds=10, name = 'pr_auc')
roc_metric = AUC(name = 'auc')
METRICS = [dice_coef,
Precision(name='precision'),
Recall(name='recall'),
pr_metric, roc_metric
]
########## Initialization of Parameters #######################
image_row = 128
image_col = 128
image_depth = 2
def resunet():
inputs = Input((image_row, image_col, image_depth))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
input_new = Conv2D(32, (1, 1), activation='relu', padding='same')(inputs)
conc1 = Add()([input_new, conv1])
pool1 = MaxPooling2D(pool_size=(2, 2))(conc1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
input_new = Conv2D(64, (1, 1), activation='relu', padding='same')(pool1)
conc2 = Add()([input_new, conv2])
pool2 = MaxPooling2D(pool_size=(2, 2))(conc2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
input_new = Conv2D(128, (1, 1), activation='relu', padding='same')(pool2)
conc3 = Add()([input_new, conv3])
drop3 = Dropout(0.5)(conc3)
pool3 = MaxPooling2D(pool_size=(2, 2))(drop3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
input_new = Conv2D(256, (1, 1), activation='relu', padding='same')(pool3)
conc4 = Add()([input_new, conv4])
drop4 = Dropout(0.5)(conc4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
input_new = Conv2D(512, (1, 1), activation='relu', padding='same')(pool4)
conc5 = Add()([input_new, conv5])
up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conc5), conv4], axis=3)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
input_new = Conv2D(256, (1, 1), activation='relu', padding='same')(up6)
conc6 = Add()([input_new, conv6])
up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conc6), conv3], axis=3)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
input_new = Conv2D(128, (1, 1), activation='relu', padding='same')(up7)
conc7 = Add()([input_new, conv7])
up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conc7), conv2], axis=3)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
input_new = Conv2D(64, (1, 1), activation='relu', padding='same')(up8)
conc8 = Add()([input_new, conv8])
up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conc8), conv1], axis=3)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
input_new = Conv2D(32, (1, 1), activation='relu', padding='same')(up9)
conc9 = Add()([input_new, conv9])
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conc9)
model = Model(inputs=[inputs], outputs=[conv10])
#model.summary()
model.compile(optimizer=Adam(lr=1e-5), loss= dice_loss, metrics=METRICS)
pretrained_weights = None
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model